{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "87ae6fa0",
   "metadata": {},
   "source": [
    "# Linear least squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3504ead1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from os.path import basename, exists\n",
    "\n",
    "\n",
    "def download(url):\n",
    "    filename = basename(url)\n",
    "    if not exists(filename):\n",
    "        from urllib.request import urlretrieve\n",
    "\n",
    "        local, _ = urlretrieve(url, filename)\n",
    "        print(\"Downloaded \" + local)\n",
    "\n",
    "\n",
    "download(\"https://github.com/AllenDowney/ThinkStats2/raw/master/code/thinkstats2.py\")\n",
    "download(\"https://github.com/AllenDowney/ThinkStats2/raw/master/code/thinkplot.py\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "586c1450",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import random\n",
    "\n",
    "import thinkstats2\n",
    "import thinkplot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b3e49c9",
   "metadata": {},
   "source": [
    "## Least squares fit\n",
    "\n",
    "Correlation coefficients measure the strength and sign of a\n",
    "relationship, but not the slope. There are several ways to estimate the\n",
    "slope; the most common is a **linear least squares fit**. A \"linear fit\"\n",
    "is a line intended to model the relationship between variables. A \"least\n",
    "squares\" fit is one that minimizes the mean squared error (MSE) between\n",
    "the line and the data.\n",
    "\n",
    "Suppose we have a sequence of points, `ys`, that we want to express as a\n",
    "function of another sequence `xs`. If there is a linear relationship\n",
    "between `xs` and `ys` with intercept `inter` and slope `slope`, we\n",
    "expect each `y[i]` to be `inter + slope * x[i]`.\n",
    "\n",
    "But unless the correlation is perfect, this prediction is only\n",
    "approximate. The vertical deviation from the line, or **residual**, is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2fa196d2",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ys' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mys\u001b[49m \u001b[38;5;241m-\u001b[39m (inter \u001b[38;5;241m+\u001b[39m slope \u001b[38;5;241m*\u001b[39m xs)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ys' is not defined"
     ]
    }
   ],
   "source": [
    "res = ys - (inter + slope * xs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb3a7b0c",
   "metadata": {},
   "source": [
    "The residuals might be due to random factors like measurement error, or\n",
    "non-random factors that are unknown. For example, if we are trying to\n",
    "predict weight as a function of height, unknown factors might include\n",
    "diet, exercise, and body type.\n",
    "\n",
    "If we get the parameters `inter` and `slope` wrong, the residuals get\n",
    "bigger, so it makes intuitive sense that the parameters we want are the\n",
    "ones that minimize the residuals.\n",
    "\n",
    "We might try to minimize the absolute value of the residuals, or their\n",
    "squares, or their cubes; but the most common choice is to minimize the\n",
    "sum of squared residuals, `sum(res**2)`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b90579",
   "metadata": {},
   "source": [
    "Why? There are three good reasons and one less important one:\n",
    "\n",
    "-   Squaring has the feature of treating positive and negative residuals\n",
    "    the same, which is usually what we want.\n",
    "\n",
    "-   Squaring gives more weight to large residuals, but not so much\n",
    "    weight that the largest residual always dominates.\n",
    "\n",
    "-   If the residuals are uncorrelated and normally distributed with mean\n",
    "    0 and constant (but unknown) variance, then the least squares fit is\n",
    "    also the maximum likelihood estimator of `inter` and `slope`. See\n",
    "    <https://en.wikipedia.org/wiki/Linear_regression>.\n",
    "\n",
    "-   The values of `inter` and `slope` that minimize the squared\n",
    "    residuals can be computed efficiently."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdd29c0a",
   "metadata": {},
   "source": [
    "The last reason made sense when computational efficiency was more\n",
    "important than choosing the method most appropriate to the problem at\n",
    "hand. That's no longer the case, so it is worth considering whether\n",
    "squared residuals are the right thing to minimize.\n",
    "\n",
    "For example, if you are using `xs` to predict values of `ys`, guessing\n",
    "too high might be better (or worse) than guessing too low. In that case\n",
    "you might want to compute some cost function for each residual, and\n",
    "minimize total cost, `sum(cost(res))`. However, computing a least\n",
    "squares fit is quick, easy and often good enough."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f5870b7",
   "metadata": {},
   "source": [
    "## Implementation\n",
    "\n",
    "`thinkstats2` provides simple functions that demonstrate linear least\n",
    "squares:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2e91598a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from thinkstats2 import Mean, MeanVar, Var, Std, Cov\n",
    "\n",
    "def LeastSquares(xs, ys):\n",
    "    meanx, varx = MeanVar(xs)\n",
    "    meany = Mean(ys)\n",
    "\n",
    "    slope = Cov(xs, ys, meanx, meany) / varx\n",
    "    inter = meany - slope * meanx\n",
    "\n",
    "    return inter, slope"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f86bc21e",
   "metadata": {},
   "source": [
    "`LeastSquares` takes sequences `xs` and `ys` and returns the estimated\n",
    "parameters `inter` and `slope`. For details on how it works, see\n",
    "<http://wikipedia.org/wiki/Numerical_methods_for_linear_least_squares>.\n",
    "\n",
    "`thinkstats2` also provides `FitLine`, which takes `inter` and `slope`\n",
    "and returns the fitted line for a sequence of `xs`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cde58c6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def FitLine(xs, inter, slope):\n",
    "    fit_xs = np.sort(xs)\n",
    "    fit_ys = inter + slope * fit_xs\n",
    "    return fit_xs, fit_ys"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "354c8975",
   "metadata": {},
   "source": [
    "We can use these functions to compute the least squares fit for birth\n",
    "weight as a function of mother's age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2a407a97",
   "metadata": {},
   "outputs": [],
   "source": [
    "download(\"https://github.com/AllenDowney/ThinkStats2/raw/master/code/nsfg.py\")\n",
    "download(\"https://github.com/AllenDowney/ThinkStats2/raw/master/code/first.py\")\n",
    "download(\"https://github.com/AllenDowney/ThinkStats2/raw/master/code/2002FemPreg.dct\")\n",
    "download(\n",
    "    \"https://github.com/AllenDowney/ThinkStats2/raw/master/code/2002FemPreg.dat.gz\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "180ec60c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import first\n",
    "\n",
    "live, firsts, others = first.MakeFrames()\n",
    "live = live.dropna(subset=['agepreg', 'totalwgt_lb'])\n",
    "ages = live.agepreg\n",
    "weights = live.totalwgt_lb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8b160317",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.830396973311053, 0.017453851471802746)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inter, slope = thinkstats2.LeastSquares(ages, weights)\n",
    "inter, slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "776f64ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_xs, fit_ys = thinkstats2.FitLine(ages, inter, slope)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "910627e6",
   "metadata": {},
   "source": [
    "The estimated intercept and slope are 6.8 lbs and 0.017 lbs per year.\n",
    "These values are hard to interpret in this form: the intercept is the\n",
    "expected weight of a baby whose mother is 0 years old, which doesn't\n",
    "make sense in context, and the slope is too small to grasp easily.\n",
    "\n",
    "Instead of presenting the intercept at $x=0$, it is often helpful to\n",
    "present the intercept at the mean of $x$. In this case the mean age is\n",
    "about 25 years and the mean baby weight for a 25 year old mother is 7.3\n",
    "pounds. The slope is 0.27 ounces per year, or 0.17 pounds per decade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fa0aaf7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Scatter(ages, weights, color='blue', alpha=0.1, s=10)\n",
    "thinkplot.Plot(fit_xs, fit_ys, color='white', linewidth=3)\n",
    "thinkplot.Plot(fit_xs, fit_ys, color='red', linewidth=2)\n",
    "thinkplot.Config(xlabel=\"Mother's age (years)\",\n",
    "                 ylabel='Birth weight (lbs)',\n",
    "                 axis=[10, 45, 0, 15],\n",
    "                 legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e961aab",
   "metadata": {},
   "source": [
    "Figure [\\[linear1\\]](#linear1){reference-type=\"ref\" reference=\"linear1\"}\n",
    "shows a scatter plot of birth weight and age along with the fitted line.\n",
    "It's a good idea to look at a figure like this to assess whether the\n",
    "relationship is linear and whether the fitted line seems like a good\n",
    "model of the relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e815f34b",
   "metadata": {},
   "source": [
    "## Residuals\n",
    "\n",
    "Another useful test is to plot the residuals. `thinkstats2` provides a\n",
    "function that computes residuals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8c854090",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Residuals(xs, ys, inter, slope):\n",
    "    xs = np.asarray(xs)\n",
    "    ys = np.asarray(ys)\n",
    "    res = ys - (inter + slope * xs)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ab39715",
   "metadata": {},
   "source": [
    "`Residuals` takes sequences `xs` and `ys` and estimated parameters\n",
    "`inter` and `slope`. It returns the differences between the actual\n",
    "values and the fitted line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dc093818",
   "metadata": {},
   "outputs": [],
   "source": [
    "live['residual'] = Residuals(ages, weights, inter, slope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eaf7696c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[15.212333333333335,\n",
       " 17.740359281437126,\n",
       " 20.506304824561404,\n",
       " 23.455752212389378,\n",
       " 26.435156146179406,\n",
       " 29.411177432542924,\n",
       " 32.30232530120482,\n",
       " 35.240273631840786,\n",
       " 38.10876470588235,\n",
       " 40.91205882352941]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.arange(10, 48, 3)\n",
    "indices = np.digitize(live.agepreg, bins)\n",
    "groups = live.groupby(indices)\n",
    "\n",
    "age_means = [group.agepreg.mean() for _, group in groups][1:-1]\n",
    "age_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d5180d24",
   "metadata": {},
   "outputs": [],
   "source": [
    "cdfs = [thinkstats2.Cdf(group.residual) for _, group in groups][1:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ac48a56b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotPercentiles(age_means, cdfs):\n",
    "    thinkplot.PrePlot(3)\n",
    "    for percent in [75, 50, 25]:\n",
    "        weight_percentiles = [cdf.Percentile(percent) for cdf in cdfs]\n",
    "        label = '%dth' % percent\n",
    "        thinkplot.Plot(age_means, weight_percentiles, label=label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b1f12380",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotPercentiles(age_means, cdfs)\n",
    "\n",
    "thinkplot.Config(xlabel=\"Mother's age (years)\",\n",
    "                 ylabel='Residual (lbs)',\n",
    "                 xlim=[10, 45])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffe97a6c",
   "metadata": {},
   "source": [
    "To visualize the residuals, I group respondents by age and compute\n",
    "percentiles in each group, as we saw in\n",
    "Section [\\[characterizing\\]](#characterizing){reference-type=\"ref\"\n",
    "reference=\"characterizing\"}.\n",
    "Figure [\\[linear2\\]](#linear2){reference-type=\"ref\" reference=\"linear2\"}\n",
    "shows the 25th, 50th and 75th percentiles of the residuals for each age\n",
    "group. The median is near zero, as expected, and the interquartile range\n",
    "is about 2 pounds. So if we know the mother's age, we can guess the\n",
    "baby's weight within a pound, about 50% of the time.\n",
    "\n",
    "Ideally these lines should be flat, indicating that the residuals are\n",
    "random, and parallel, indicating that the variance of the residuals is\n",
    "the same for all age groups. In fact, the lines are close to parallel,\n",
    "so that's good; but they have some curvature, indicating that the\n",
    "relationship is nonlinear. Nevertheless, the linear fit is a simple\n",
    "model that is probably good enough for some purposes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7be8b43c",
   "metadata": {},
   "source": [
    "## Estimation\n",
    "\n",
    "The parameters `slope` and `inter` are estimates based on a sample; like\n",
    "other estimates, they are vulnerable to sampling bias, measurement\n",
    "error, and sampling error. As discussed in\n",
    "Chapter [\\[estimation\\]](#estimation){reference-type=\"ref\"\n",
    "reference=\"estimation\"}, sampling bias is caused by non-representative\n",
    "sampling, measurement error is caused by errors in collecting and\n",
    "recording data, and sampling error is the result of measuring a sample\n",
    "rather than the entire population.\n",
    "\n",
    "To assess sampling error, we ask, \"If we run this experiment again, how\n",
    "much variability do we expect in the estimates?\" We can answer this\n",
    "question by running simulated experiments and computing sampling\n",
    "distributions of the estimates.\n",
    "\n",
    "I simulate the experiments by resampling the data; that is, I treat the\n",
    "observed pregnancies as if they were the entire population and draw\n",
    "samples, with replacement, from the observed sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "410d9204",
   "metadata": {},
   "outputs": [],
   "source": [
    "def SampleRows(df, nrows, replace=False):\n",
    "    \"\"\"Choose a sample of rows from a DataFrame.\n",
    "\n",
    "    df: DataFrame\n",
    "    nrows: number of rows\n",
    "    replace: whether to sample with replacement\n",
    "\n",
    "    returns: DataDf\n",
    "    \"\"\"\n",
    "    indices = np.random.choice(df.index, nrows, replace=replace)\n",
    "    sample = df.loc[indices]\n",
    "    return sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "946cb0af",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ResampleRows(df):\n",
    "    \"\"\"Resamples rows from a DataFrame.\n",
    "\n",
    "    df: DataFrame\n",
    "\n",
    "    returns: DataFrame\n",
    "    \"\"\"\n",
    "    return SampleRows(df, len(df), replace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a6382b58",
   "metadata": {},
   "outputs": [],
   "source": [
    "def SamplingDistributions(live, iters=101):\n",
    "    t = []\n",
    "    for _ in range(iters):\n",
    "        sample = thinkstats2.ResampleRows(live)\n",
    "        ages = sample.agepreg\n",
    "        weights = sample.totalwgt_lb\n",
    "        estimates = thinkstats2.LeastSquares(ages, weights)\n",
    "        t.append(estimates)\n",
    "\n",
    "    inters, slopes = zip(*t)\n",
    "    return inters, slopes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5584d4dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "inters, slopes = SamplingDistributions(live, iters=1001)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b74a99e",
   "metadata": {},
   "source": [
    "`SamplingDistributions` takes a DataFrame with one row per live birth,\n",
    "and `iters`, the number of experiments to simulate. It uses\n",
    "`ResampleRows` to resample the observed pregnancies. We've already seen\n",
    "`SampleRows`, which chooses random rows from a DataFrame. `thinkstats2`\n",
    "also provides `ResampleRows`, which returns a sample the same size as\n",
    "the original:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eb967d0",
   "metadata": {},
   "source": [
    "After resampling, we use the simulated sample to estimate parameters.\n",
    "The result is two sequences: the estimated intercepts and estimated\n",
    "slopes.\n",
    "\n",
    "I summarize the sampling distributions by printing the standard error\n",
    "and confidence interval:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c92f2210",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Summarize(estimates, actual=None):\n",
    "    mean = thinkstats2.Mean(estimates)\n",
    "    stderr = thinkstats2.Std(estimates, mu=actual)\n",
    "    cdf = thinkstats2.Cdf(estimates)\n",
    "    ci = cdf.ConfidenceInterval(90)\n",
    "    print('mean, SE, CI', mean, stderr, ci)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be947b14",
   "metadata": {},
   "source": [
    "`Summarize` takes a sequence of estimates and the actual value. It\n",
    "prints the mean of the estimates, the standard error and a 90%\n",
    "confidence interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7e76726f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean, SE, CI 6.83346967384324 0.06772233597164748 (6.724508263178181, 6.946444466395725)\n"
     ]
    }
   ],
   "source": [
    "Summarize(inters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "da1a80f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean, SE, CI 0.01732961674795306 0.00267138995054031 (0.012838271847846819, 0.0217432188488668)\n"
     ]
    }
   ],
   "source": [
    "Summarize(slopes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d3e0bef",
   "metadata": {},
   "source": [
    "For the intercept, the mean estimate is 6.83, with standard error 0.07\n",
    "and 90% confidence interval (6.71, 6.94). The estimated slope, in more\n",
    "compact form, is 0.0174, SE 0.0028, CI (0.0126, 0.0220). There is almost\n",
    "a factor of two between the low and high ends of this CI, so it should\n",
    "be considered a rough estimate.\n",
    "\n",
    "To visualize the sampling error of the estimate, we could plot all of\n",
    "the fitted lines, or for a less cluttered representation, plot a 90%\n",
    "confidence interval for each age. Here's the code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b6075384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for slope, inter in zip(slopes, inters):\n",
    "    fxs, fys = FitLine(age_means, inter, slope)\n",
    "    thinkplot.Plot(fxs, fys, color='gray', alpha=0.01)\n",
    "    \n",
    "thinkplot.Config(xlabel=\"Mother's age (years)\",\n",
    "                 ylabel='Residual (lbs)',\n",
    "                 xlim=[10, 45])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3f2f2757",
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotConfidenceIntervals(xs, inters, slopes,\n",
    "                            percent=90, **options):\n",
    "    fys_seq = []\n",
    "    for inter, slope in zip(inters, slopes):\n",
    "        fxs, fys = thinkstats2.FitLine(xs, inter, slope)\n",
    "        fys_seq.append(fys)\n",
    "\n",
    "    p = (100 - percent) / 2\n",
    "    percents = p, 100 - p\n",
    "    low, high = thinkstats2.PercentileRows(fys_seq, percents)\n",
    "    thinkplot.FillBetween(fxs, low, high, **options)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47c3826c",
   "metadata": {},
   "source": [
    "`xs` is the sequence of mother's age. `inters` and `slopes` are the\n",
    "estimated parameters generated by `SamplingDistributions`. `percent`\n",
    "indicates which confidence interval to plot.\n",
    "\n",
    "`PlotConfidenceIntervals` generates a fitted line for each pair of\n",
    "`inter` and `slope` and stores the results in a sequence, `fys_seq`.\n",
    "Then it uses `PercentileRows` to select the upper and lower percentiles\n",
    "of `y` for each value of `x`. For a 90% confidence interval, it selects\n",
    "the 5th and 95th percentiles. `FillBetween` draws a polygon that fills\n",
    "the space between two lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9bd7bbc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotConfidenceIntervals(age_means, inters, slopes, percent=90, \n",
    "                        color='gray', alpha=0.3, label='90% CI')\n",
    "PlotConfidenceIntervals(age_means, inters, slopes, percent=50,\n",
    "                        color='gray', alpha=0.5, label='50% CI')\n",
    "\n",
    "thinkplot.Config(xlabel=\"Mother's age (years)\",\n",
    "                 ylabel='Residual (lbs)',\n",
    "                 xlim=[10, 45])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f4a2608",
   "metadata": {},
   "source": [
    "Figure [\\[linear3\\]](#linear3){reference-type=\"ref\" reference=\"linear3\"}\n",
    "shows the 50% and 90% confidence intervals for curves fitted to birth\n",
    "weight as a function of mother's age. The vertical width of the region\n",
    "represents the effect of sampling error; the effect is smaller for\n",
    "values near the mean and larger for the extremes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "893dc012",
   "metadata": {},
   "source": [
    "## Goodness of fit\n",
    "\n",
    "There are several ways to measure the quality of a linear model, or\n",
    "**goodness of fit**. One of the simplest is the standard deviation of\n",
    "the residuals.\n",
    "\n",
    "If you use a linear model to make predictions, `Std(res)` is the root\n",
    "mean squared error (RMSE) of your predictions. For example, if you use\n",
    "mother's age to guess birth weight, the RMSE of your guess would be 1.40\n",
    "lbs.\n",
    "\n",
    "If you guess birth weight without knowing the mother's age, the RMSE of\n",
    "your guess is `Std(ys)`, which is 1.41 lbs. So in this example, knowing\n",
    "a mother's age does not improve the predictions substantially.\n",
    "\n",
    "Another way to measure goodness of fit is the **coefficient of\n",
    "determination**, usually denoted $R^2$ and called \"R-squared\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5566d09c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def CoefDetermination(ys, res):\n",
    "    return 1 - Var(res) / Var(ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6e6315ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.004738115474710258"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inter, slope = LeastSquares(ages, weights)\n",
    "res = Residuals(ages, weights, inter, slope)\n",
    "r2 = CoefDetermination(weights, res)\n",
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fddcaab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rho 0.06883397035410904\n",
      "R 0.06883397035410828\n"
     ]
    }
   ],
   "source": [
    "print('rho', thinkstats2.Corr(ages, weights))\n",
    "print('R', np.sqrt(r2))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5320cfdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Std(ys) 1.40821553384062\n",
      "Std(res) 1.4048754287857834\n"
     ]
    }
   ],
   "source": [
    "print('Std(ys)', Std(weights))\n",
    "print('Std(res)', Std(res))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dba7079",
   "metadata": {},
   "source": [
    "`Var(res)` is the MSE of your guesses using the model, `Var(ys)` is the\n",
    "MSE without it. So their ratio is the fraction of MSE that remains if\n",
    "you use the model, and $R^2$ is the fraction of MSE the model\n",
    "eliminates.\n",
    "\n",
    "For birth weight and mother's age, $R^2$ is 0.0047, which means that\n",
    "mother's age predicts about half of 1% of variance in birth weight.\n",
    "\n",
    "There is a simple relationship between the coefficient of determination\n",
    "and Pearson's coefficient of correlation: $R^2 = \\rho^2$. For example,\n",
    "if $\\rho$ is 0.8 or -0.8, $R^2 = 0.64$.\n",
    "\n",
    "Although $\\rho$ and $R^2$ are often used to quantify the strength of a\n",
    "relationship, they are not easy to interpret in terms of predictive\n",
    "power. In my opinion, `Std(res)` is the best representation of the\n",
    "quality of prediction, especially if it is presented in relation to\n",
    "`Std(ys)`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90a43862",
   "metadata": {},
   "source": [
    "For example, when people talk about the validity of the SAT (a\n",
    "standardized test used for college admission in the U.S.) they often\n",
    "talk about correlations between SAT scores and other measures of\n",
    "intelligence.\n",
    "\n",
    "According to one study, there is a Pearson correlation of $\\rho=0.72$\n",
    "between total SAT scores and IQ scores, which sounds like a strong\n",
    "correlation. But $R^2 = \\rho^2 = 0.52$, so SAT scores account for only\n",
    "52% of variance in IQ.\n",
    "\n",
    "IQ scores are normalized with `Std(ys) = 15`, so"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7831695d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.409610943738484"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_ys = 15**2\n",
    "rho = 0.72\n",
    "r2 = rho**2\n",
    "var_res = (1 - r2) * var_ys\n",
    "std_res = np.sqrt(var_res)\n",
    "std_res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71196000",
   "metadata": {},
   "source": [
    "So using SAT score to predict IQ reduces RMSE from 15 points to 10.4\n",
    "points. A correlation of 0.72 yields a reduction in RMSE of only 31%.\n",
    "\n",
    "If you see a correlation that looks impressive, remember that $R^2$ is a\n",
    "better indicator of reduction in MSE, and reduction in RMSE is a better\n",
    "indicator of predictive power."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a544610",
   "metadata": {},
   "source": [
    "## Testing a linear model\n",
    "\n",
    "The effect of mother's age on birth weight is small, and has little\n",
    "predictive power. So is it possible that the apparent relationship is\n",
    "due to chance? There are several ways we might test the results of a\n",
    "linear fit.\n",
    "\n",
    "One option is to test whether the apparent reduction in MSE is due to\n",
    "chance. In that case, the test statistic is $R^2$ and the null\n",
    "hypothesis is that there is no relationship between the variables. We\n",
    "can simulate the null hypothesis by permutation, as in\n",
    "Section [\\[corrtest\\]](#corrtest){reference-type=\"ref\"\n",
    "reference=\"corrtest\"}, when we tested the correlation between mother's\n",
    "age and birth weight. In fact, because $R^2 = \\rho^2$, a one-sided test\n",
    "of $R^2$ is equivalent to a two-sided test of $\\rho$. We've already done\n",
    "that test, and found $p < 0.001$, so we conclude that the apparent\n",
    "relationship between mother's age and birth weight is statistically\n",
    "significant."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6dafb9f",
   "metadata": {},
   "source": [
    "Another approach is to test whether the apparent slope is due to chance.\n",
    "The null hypothesis is that the slope is actually zero; in that case we\n",
    "can model the birth weights as random variations around their mean.\n",
    "Here's a HypothesisTest for this model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8e4c6680",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SlopeTest(thinkstats2.HypothesisTest):\n",
    "\n",
    "    def TestStatistic(self, data):\n",
    "        ages, weights = data\n",
    "        _, slope = thinkstats2.LeastSquares(ages, weights)\n",
    "        return slope\n",
    "\n",
    "    def MakeModel(self):\n",
    "        _, weights = self.data\n",
    "        self.ybar = weights.mean()\n",
    "        self.res = weights - self.ybar\n",
    "\n",
    "    def RunModel(self):\n",
    "        ages, _ = self.data\n",
    "        weights = self.ybar + np.random.permutation(self.res)\n",
    "        return ages, weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0eb3c6e",
   "metadata": {},
   "source": [
    "The data are represented as sequences of ages and weights. The test\n",
    "statistic is the slope estimated by `LeastSquares`. The model of the\n",
    "null hypothesis is represented by the mean weight of all babies and the\n",
    "deviations from the mean. To generate simulated data, we permute the\n",
    "deviations and add them to the mean.\n",
    "\n",
    "Here's the code that runs the hypothesis test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7a0526fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "live, firsts, others = first.MakeFrames()\n",
    "live = live.dropna(subset=['agepreg', 'totalwgt_lb'])\n",
    "ht = SlopeTest((live.agepreg, live.totalwgt_lb))\n",
    "p_value = ht.PValue()\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dfefce14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.017453851471802746, 0.008602477027343606)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ht.actual, ht.MaxTestStat()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dc74eae",
   "metadata": {},
   "source": [
    "The p-value is less than $0.001$, so although the estimated slope is\n",
    "small, it is unlikely to be due to chance.\n",
    "\n",
    "Estimating the p-value by simulating the null hypothesis is strictly\n",
    "correct, but there is a simpler alternative. Remember that we already\n",
    "computed the sampling distribution of the slope, in\n",
    "Section [\\[regest\\]](#regest){reference-type=\"ref\" reference=\"regest\"}.\n",
    "To do that, we assumed that the observed slope was correct and simulated\n",
    "experiments by resampling.\n",
    "\n",
    "Figure [\\[linear4\\]](#linear4){reference-type=\"ref\" reference=\"linear4\"}\n",
    "shows the sampling distribution of the slope, from\n",
    "Section [\\[regest\\]](#regest){reference-type=\"ref\" reference=\"regest\"},\n",
    "and the distribution of slopes generated under the null hypothesis. The\n",
    "sampling distribution is centered about the estimated slope, 0.017\n",
    "lbs/year, and the slopes under the null hypothesis are centered around\n",
    "0; but other than that, the distributions are identical. The\n",
    "distributions are also symmetric, for reasons we will see in\n",
    "Section [\\[CLT\\]](#CLT){reference-type=\"ref\" reference=\"CLT\"}."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2a60bc85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sampling_cdf = thinkstats2.Cdf(slopes)\n",
    "\n",
    "thinkplot.PrePlot(2)\n",
    "thinkplot.Plot([0, 0], [0, 1], color='0.8')\n",
    "ht.PlotCdf(label='null hypothesis')\n",
    "\n",
    "thinkplot.Cdf(sampling_cdf, label='sampling distribution')\n",
    "\n",
    "thinkplot.Config(xlabel='slope (lbs / year)',\n",
    "                   ylabel='CDF',\n",
    "                   xlim=[-0.03, 0.03],\n",
    "                   legend=True, loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94e7ce3c",
   "metadata": {},
   "source": [
    "So we could estimate the p-value two ways:\n",
    "\n",
    "-   Compute the probability that the slope under the null hypothesis\n",
    "    exceeds the observed slope.\n",
    "\n",
    "-   Compute the probability that the slope in the sampling distribution\n",
    "    falls below 0. (If the estimated slope were negative, we would\n",
    "    compute the probability that the slope in the sampling distribution\n",
    "    exceeds 0.)\n",
    "\n",
    "The second option is easier because we normally want to compute the\n",
    "sampling distribution of the parameters anyway. And it is a good\n",
    "approximation unless the sample size is small *and* the distribution of\n",
    "residuals is skewed. Even then, it is usually good enough, because\n",
    "p-values don't have to be precise.\n",
    "\n",
    "Here's the code that estimates the p-value of the slope using the\n",
    "sampling distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ff42728b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inters, slopes = SamplingDistributions(live, iters=1001)\n",
    "slope_cdf = thinkstats2.Cdf(slopes)\n",
    "p_value = slope_cdf[0]\n",
    "p_value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4a79064",
   "metadata": {},
   "source": [
    "Again, we find $p < 0.001$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73488248",
   "metadata": {},
   "source": [
    "## Weighted resampling\n",
    "\n",
    "So far we have treated the NSFG data as if it were a representative\n",
    "sample, but as I mentioned in\n",
    "Section [\\[nsfg\\]](#nsfg){reference-type=\"ref\" reference=\"nsfg\"}, it is\n",
    "not. The survey deliberately oversamples several groups in order to\n",
    "improve the chance of getting statistically significant results; that\n",
    "is, in order to improve the power of tests involving these groups.\n",
    "\n",
    "This survey design is useful for many purposes, but it means that we\n",
    "cannot use the sample to estimate values for the general population\n",
    "without accounting for the sampling process.\n",
    "\n",
    "For each respondent, the NSFG data includes a variable called\n",
    "`finalwgt`, which is the number of people in the general population the\n",
    "respondent represents. This value is called a **sampling weight**, or\n",
    "just \"weight.\"\n",
    "\n",
    "As an example, if you survey 100,000 people in a country of 300 million,\n",
    "each respondent represents 3,000 people. If you oversample one group by\n",
    "a factor of 2, each person in the oversampled group would have a lower\n",
    "weight, about 1500."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9cdc9ef",
   "metadata": {},
   "source": [
    "To correct for oversampling, we can use resampling; that is, we can draw\n",
    "samples from the survey using probabilities proportional to sampling\n",
    "weights. Then, for any quantity we want to estimate, we can generate\n",
    "sampling distributions, standard errors, and confidence intervals. As an\n",
    "example, I will estimate mean birth weight with and without sampling\n",
    "weights.\n",
    "\n",
    "In Section [\\[regest\\]](#regest){reference-type=\"ref\"\n",
    "reference=\"regest\"}, we saw `ResampleRows`, which chooses rows from a\n",
    "DataFrame, giving each row the same probability. Now we need to do the\n",
    "same thing using probabilities proportional to sampling weights.\n",
    "`ResampleRowsWeighted` takes a DataFrame, resamples rows according to\n",
    "the weights in `finalwgt`, and returns a DataFrame containing the\n",
    "resampled rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6ccf2ed3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ResampleRowsWeighted(df, column='finalwgt'):\n",
    "    weights = df[column]\n",
    "    cdf = thinkstats2.Cdf(dict(weights))\n",
    "    indices = cdf.Sample(len(weights))\n",
    "    sample = df.loc[indices]\n",
    "    return sample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac6489e",
   "metadata": {},
   "source": [
    "`weights` is a Series; converting it to a dictionary makes a map from\n",
    "the indices to the weights. In `cdf` the values are indices and the\n",
    "probabilities are proportional to the weights.\n",
    "\n",
    "`indices` is a sequence of row indices; `sample` is a DataFrame that\n",
    "contains the selected rows. Since we sample with replacement, the same\n",
    "row might appear more than once.\n",
    "\n",
    "Now we can compare the effect of resampling with and without weights.\n",
    "Without weights, we generate the sampling distribution like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b3f63d9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean, SE, CI 7.348876203252929 0.01403009016964961 (7.320812126576676, 7.372144003098031)\n"
     ]
    }
   ],
   "source": [
    "iters = 100\n",
    "estimates = [ResampleRowsWeighted(live).totalwgt_lb.mean()\n",
    "             for _ in range(iters)]\n",
    "Summarize(estimates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da151467",
   "metadata": {},
   "source": [
    "With weights, it looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ffdd06ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean, SE, CI 7.265405371763665 0.013719971782041939 (7.243389024120381, 7.285226266873202)\n"
     ]
    }
   ],
   "source": [
    "estimates = [thinkstats2.ResampleRows(live).totalwgt_lb.mean()\n",
    "             for _ in range(iters)]\n",
    "Summarize(estimates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7484b502",
   "metadata": {},
   "source": [
    "The following table summarizes the results:\n",
    "\n",
    " ```\n",
    "  ------------ -------------- ---------- --------------\n",
    "                 mean birth    standard      90% CI\n",
    "                weight (lbs)    error    \n",
    "  Unweighted        7.27        0.014     (7.24, 7.29)\n",
    "  Weighted          7.35        0.014     (7.32, 7.37)\n",
    "  ------------ -------------- ---------- --------------\n",
    "```\n",
    "\n",
    "In this example, the effect of weighting is small but non-negligible.\n",
    "The difference in estimated means, with and without weighting, is about\n",
    "0.08 pounds, or 1.3 ounces. This difference is substantially larger than\n",
    "the standard error of the estimate, 0.014 pounds, which implies that the\n",
    "difference is not due to chance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "404ac2e8",
   "metadata": {},
   "source": [
    "## Glossary\n",
    "\n",
    "-   **linear fit**: a line intended to model the relationship between\n",
    "    variables.\n",
    "\n",
    "-   **least squares fit**: A model of a dataset that minimizes the sum\n",
    "    of squares of the residuals.\n",
    "\n",
    "-   **residual**: The deviation of an actual value from a model.\n",
    "\n",
    "-   **goodness of fit**: A measure of how well a model fits data.\n",
    "\n",
    "-   **coefficient of determination**: A statistic intended to quantify\n",
    "    goodness of fit.\n",
    "\n",
    "-   **sampling weight**: A value associated with an observation in a\n",
    "    sample that indicates what part of the population it represents."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f27d748b",
   "metadata": {},
   "source": [
    "# Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f90970e",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Exercise:** Use `ResampleRows` and generate a list of estimates for the mean birth weight.  Use `Summarize` to compute the SE and CI for these estimates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6f158404",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean, SE, CI 7.265603770192521 0.014495100738282139 (7.2411069926975, 7.2891057202921)\n"
     ]
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "iters = 1000\n",
    "estimates = [ResampleRows(live).totalwgt_lb.mean()\n",
    "             for _ in range(iters)]\n",
    "Summarize(estimates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63c2d949",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The difference is non-negligible, which suggests that there are differences in birth weight between the strata in the survey."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3a87cef",
   "metadata": {},
   "source": [
    "**Exercise:** Using the data from the BRFSS, compute the linear least squares fit for log(weight) versus height. How would you best present the estimated parameters for a model like this where one of the variables is log-transformed? If you were trying to guess someone’s weight, how much would it help to know their height?\n",
    "\n",
    "Like the NSFG, the BRFSS oversamples some groups and provides a sampling weight for each respondent. In the BRFSS data, the variable name for these weights is totalwt. Use resampling, with and without weights, to estimate the mean height of respondents in the BRFSS, the standard error of the mean, and a 90% confidence interval. How much does correct weighting affect the estimates?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fd6ca05",
   "metadata": {},
   "source": [
    "Read the BRFSS data and extract heights and log weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e375d3ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "download(\"https://github.com/AllenDowney/ThinkStats2/raw/master/code/brfss.py\")\n",
    "download(\"https://github.com/AllenDowney/ThinkStats2/raw/master/code/CDBRFS08.ASC.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ce2bc21b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import brfss\n",
    "\n",
    "df = brfss.ReadBrfss(nrows=None)\n",
    "df = df.dropna(subset=['htm3', 'wtkg2'])\n",
    "heights, weights = df.htm3, df.wtkg2\n",
    "log_weights = np.log10(weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b4c4d1",
   "metadata": {},
   "source": [
    "Estimate intercept and slope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "91ee8f19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9930804163932864, 0.005281454169417784)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "inter, slope = thinkstats2.LeastSquares(heights, log_weights)\n",
    "inter, slope"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3f97270",
   "metadata": {},
   "source": [
    "Make a scatter plot of the data and show the fitted line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f6234c76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "thinkplot.Scatter(heights, log_weights, alpha=0.01, s=5)\n",
    "fxs, fys = thinkstats2.FitLine(heights, inter, slope)\n",
    "thinkplot.Plot(fxs, fys, color='red')\n",
    "thinkplot.Config(xlabel='Height (cm)', ylabel='log10 weight (kg)', legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8fd25c1",
   "metadata": {},
   "source": [
    "Make the same plot but apply the inverse transform to show weights on a linear (not log) scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f8fe4970",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "thinkplot.Scatter(heights, weights, alpha=0.01, s=5)\n",
    "fxs, fys = thinkstats2.FitLine(heights, inter, slope)\n",
    "thinkplot.Plot(fxs, 10**fys, color='red')\n",
    "thinkplot.Config(xlabel='Height (cm)', ylabel='Weight (kg)', legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5434c525",
   "metadata": {},
   "source": [
    "Plot percentiles of the residuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1d2b06d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "# The lines are flat over most of the range, \n",
    "# indicating that the relationship is linear.\n",
    "\n",
    "# The lines are mostly parallel, indicating \n",
    "# that the variance of the residuals is the \n",
    "# same over the range.\n",
    "\n",
    "res = thinkstats2.Residuals(heights, log_weights, inter, slope)\n",
    "df['residual'] = res\n",
    "\n",
    "bins = np.arange(130, 210, 5)\n",
    "indices = np.digitize(df.htm3, bins)\n",
    "groups = df.groupby(indices)\n",
    "\n",
    "means = [group.htm3.mean() for i, group in groups][1:-1]\n",
    "cdfs = [thinkstats2.Cdf(group.residual) for i, group in groups][1:-1]\n",
    "\n",
    "thinkplot.PrePlot(3)\n",
    "for percent in [75, 50, 25]:\n",
    "    ys = [cdf.Percentile(percent) for cdf in cdfs]\n",
    "    label = '%dth' % percent\n",
    "    thinkplot.Plot(means, ys, label=label)\n",
    "    \n",
    "thinkplot.Config(xlabel='height (cm)', ylabel='residual weight (kg)', legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b974ed8",
   "metadata": {},
   "source": [
    "Compute correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1f246543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5317282605983439"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "rho = thinkstats2.Corr(heights, log_weights)\n",
    "rho"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fabe08de",
   "metadata": {},
   "source": [
    "Compute coefficient of determination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "db91483f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28273494311893743"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "r2 = thinkstats2.CoefDetermination(log_weights, res)\n",
    "r2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8adc821",
   "metadata": {},
   "source": [
    "Confirm that $R^2 = \\rho^2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "36f613c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "np.isclose(rho**2, r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e905a590",
   "metadata": {},
   "source": [
    "Compute Std(ys), which is the RMSE of predictions that don't use height."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "909dedd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10320725030004871"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "std_ys = thinkstats2.Std(log_weights)\n",
    "std_ys"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7fb039f",
   "metadata": {},
   "source": [
    "Compute Std(res), the RMSE of predictions that do use height."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b9cfa1e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08740777080416084"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "std_res = thinkstats2.Std(res)\n",
    "std_res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4769693",
   "metadata": {},
   "source": [
    "How much does height information reduce RMSE?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f99d960b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15308497658793263"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "1 - std_res / std_ys"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8faa6745",
   "metadata": {},
   "source": [
    "Use resampling to compute sampling distributions for inter and slope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "14022587",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "t = []\n",
    "for _ in range(100):\n",
    "    sample = thinkstats2.ResampleRows(df)\n",
    "    estimates = thinkstats2.LeastSquares(sample.htm3, np.log10(sample.wtkg2))\n",
    "    t.append(estimates)\n",
    "\n",
    "inters, slopes = zip(*t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef667108",
   "metadata": {},
   "source": [
    "Plot the sampling distribution of slope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "36a23bf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'xscale': 'linear', 'yscale': 'linear'}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "cdf = thinkstats2.Cdf(slopes)\n",
    "thinkplot.Cdf(cdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "890c9ae5",
   "metadata": {},
   "source": [
    "Compute the p-value of the slope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ed1c25f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "pvalue = cdf[0]\n",
    "pvalue"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acf24a1c",
   "metadata": {},
   "source": [
    "Compute the 90% confidence interval of slope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "69439610",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.005258814891351415, 0.0053051042459573575)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "ci = cdf.Percentile(5), cdf.Percentile(95)\n",
    "ci"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5a3e13",
   "metadata": {},
   "source": [
    "Compute the mean of the sampling distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b43de1f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005283086910424788"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "mean = thinkstats2.Mean(slopes)\n",
    "mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ab254fe",
   "metadata": {},
   "source": [
    "Compute the standard deviation of the sampling distribution, which is the standard error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2fb86ff0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3057180659600348e-05"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "stderr = thinkstats2.Std(slopes)\n",
    "stderr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dab9e81a",
   "metadata": {},
   "source": [
    "Resample rows without weights, compute mean height, and summarize results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4380e9f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean, SE, CI 168.95743113239962 0.018414478243185595 (168.92487216799046, 168.9835965763253)\n"
     ]
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "estimates_unweighted = [thinkstats2.ResampleRows(df).htm3.mean() for _ in range(100)]\n",
    "Summarize(estimates_unweighted)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceb857ce",
   "metadata": {},
   "source": [
    "Resample rows with weights.  Note that the weight column in this dataset is called `finalwt`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "fb52b7fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean, SE, CI 170.49303570706763 0.016607219351484338 (170.46374724630653, 170.5172851108551)\n"
     ]
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "# The estimated mean height is almost 2 cm taller \n",
    "# if we take into account the sampling weights,\n",
    "# and this difference is much bigger than the sampling error.\n",
    "\n",
    "estimates_weighted = [ResampleRowsWeighted(df, 'finalwt').htm3.mean() for _ in range(100)]\n",
    "Summarize(estimates_weighted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc18c928",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
